code: def v1_study_suggestions(request, study_name):
      # Create the trial
      if request.method == "POST":
          data = json.loads(request.body)
          trials_number = 1
  
          # TODO: Use the trial name to create trial object
          trial_name = "Trial"
          if "trials_number" in data:
              trials_number = data["trials_number"]
          if "trial_name" in data:
              trial_name = data["trial_name"]
  
          study = Study.objects.get(name=study_name)
          trials = Trial.objects.filter(study_name=study_name)
          trials = [trial for trial in trials]
  
          if study.algorithm == "RandomSearch":
              algorithm = RandomSearchAlgorithm()
          elif study.algorithm == "GridSearch":
              algorithm = GridSearchAlgorithm()
          elif study.algorithm == "BayesianOptimization":
              algorithm = BayesianOptimization()
          elif study.algorithm == "TPE":
              algorithm = TpeAlgorithm()
          elif study.algorithm == "HyperoptRandomSearch":
              algorithm = HyperoptRandomSearchAlgorithm()
          elif study.algorithm == "SimulateAnneal":
              algorithm = SimulateAnnealAlgorithm()
          elif study.algorithm == "QuasiRandomSearch":
              algorithm = QuasiRandomSearchAlgorithm()
          elif study.algorithm == "ChocolateRandomSearch":
              algorithm = ChocolateRandomSearchAlgorithm()
          elif study.algorithm == "ChocolateGridSearch":
              algorithm = ChocolateGridSearchAlgorithm()
          elif study.algorithm == "ChocolateBayes":
              algorithm = ChocolateBayesAlgorithm()
          elif study.algorithm == "CMAES":
              algorithm = CmaesAlgorithm()
          elif study.algorithm == "MOCMAES":
              algorithm = MocmaesAlgorithm()
          elif study.algorithm == "SkoptBayesianOptimization":
              algorithm = SkoptBayesianOptimization()
          else:
              return JsonResponse(
                  {
                      "error": "Unknown algorithm: {}".format(
                          study.algorithm
                      )
                  }
              )
  
          new_trials = algorithm.get_new_suggestions(
              study.name, trials, trials_number
          )
  
          return JsonResponse(
              {"data": [trial.to_json() for trial in new_trials]}
          )
      else:
          return JsonResponse({"error": "Unsupported http method"})
  
imports: from __future__ import unicode_literals
  import json
  from django.http import JsonResponse
  from django.views.decorators.csrf import csrf_exempt
  from suggestion.models import Study
  from suggestion.models import Trial
  from suggestion.models import TrialMetric
  from suggestion.algorithm.random_search import RandomSearchAlgorithm
  from suggestion.algorithm.grid_search import GridSearchAlgorithm
  from suggestion.algorithm.bayesian_optimization import BayesianOptimization
  from suggestion.algorithm.tpe import TpeAlgorithm
  from suggestion.algorithm.simulate_anneal import SimulateAnnealAlgorithm
  from suggestion.algorithm.hyperopt_random_search import HyperoptRandomSearchAlgorithm
  from suggestion.algorithm.quasi_random_search import QuasiRandomSearchAlgorithm
  from suggestion.algorithm.chocolate_random_search import ChocolateRandomSearchAlgorithm
  from suggestion.algorithm.chocolate_grid_search import ChocolateGridSearchAlgorithm
  from suggestion.algorithm.chocolate_bayes import ChocolateBayesAlgorithm
  from suggestion.algorithm.cmaes import CmaesAlgorithm
  from suggestion.algorithm.mocmaes import MocmaesAlgorithm
  from suggestion.algorithm.skopt_bayesian_optimization import SkoptBayesianOptimization
label_desc: Use image node_id 5 for calling a global function with example usage: v1_study_suggestions(request, study_name) and returns: JsonResponse, JsonResponse, JsonResponse